Description

Input Arguments

ts

The timeseries object for which you want
the standard deviation of the data.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments.
Name is the argument
name and Value is the corresponding
value. Name must appear
inside single quotes (' ').
You can specify several name and value pair
arguments in any order as Name1,Value1,...,NameN,ValueN.

'MissingData'

A string specifying one of two possible values, remove or interpolate,
indicating how to treat missing data during the calculation.

Default: remove

'Quality'

A vector of integers, indicating which quality codes represent
missing samples (for vector data) or missing observations (for data
arrays with two or more dimensions).

'Weighting'

A string specifying one of two possible values, none or time. When you specify time, larger time values
correspond to larger weights.

Output Arguments

ts_std

The standard deviation of ts.Data values
as follows:

When ts.Data is a vector, ts_std is
the standard deviation of ts.Data values.

When ts.Data is a matrix, and IsTimeFirst is true,
and the first dimension of ts is aligned with time,
then ts_std is the standard deviation of each column
of ts.Data.

When ts.Data is an N-dimensional array, std always
operates along the first nonsingleton dimension of ts.Data.

Examples

The following example finds the standard deviation for a timeseries object. MATLAB® calculates
the standard deviation for each data column in the timeseries object.

Algorithms

MATLAB determines weighting by:

Attaching a weighting to each time value, depending
on its order, as follows:

First time point — The duration of the first
time interval (t(2) - t(1)).

Time point that is neither the first nor last time
point — The duration between the midpoint of the previous time
interval to the midpoint of the subsequent time interval ((t(k
+ 1) - t(k))/2 + (t(k) - t(k - 1))/2).

Last time point — The duration of the last
time interval (t(end) - t(end - 1)).

Normalizing the weighting for each time by dividing
each weighting by the mean of all weightings.

Note:
If the timeseries object is uniformly sampled,
then the normalized weighting for each time is 1.0. Therefore, time
weighting has no effect.